Investigating Risk Adjustment Methods for Health Care Provider Profiling When Observations are Scarce or Events Rare

Abstract

Background: When profiling health care providers, adjustment for case-mix is essential. However, conventional risk adjustment methods may perform poorly, especially when provider volumes are small or events rare. Propensity score (PS) methods, commonly used in observational studies of binary treatments, have been shown to perform well when the amount of observations and/or events are low and can be extended to a multiple provider setting. The objective of this study was to evaluate the performance of different risk adjustment methods when profiling multiple health care providers that perform highly protocolized procedures, such as coronary artery bypass grafting. Methods: In a simulation study, provider effects estimated using PS adjustment, PS weighting, PS matching, and multivariable logistic regression were compared in terms of bias, coverage and mean squared error (MSE) when varying the event rate, sample size, provider volumes, and number of providers. An empirical example from the field of cardiac surgery was used to demonstrate the different methods. Results: Overall, PS adjustment, PS weighting, and logistic regression resulted in provider effects with low amounts of bias and good coverage. The PS matching and PS weighting with trimming led to biased effects and high MSE across several scenarios. Moreover, PS matching is not practical to implement when the number of providers surpasses three. Conclusions: None of the PS methods clearly outperformed logistic regression, except when sample sizes were relatively small. Propensity score matching performed worse than the other PS methods considered.